Selected reading: Cross-environment assumptions and invariance claims.
Open the source ↗How to study this source
Can a model learn causes?
Distinguish the optimisation proposal from a guarantee of causal identification.
Ideas and questions
Read background definitions when a term blocks the argument. Then return to the source and reconstruct its claim in your own words.
Read alongside, read against
Causal Inference: What If
Hernán · Robins. Compare assumptions, evidence and scope with the source above. These are editorial companions, not necessarily direct responses.
Toward Causal Representation Learning
Schölkopf et al.. Compare assumptions, evidence and scope with the source above. These are editorial companions, not necessarily direct responses.
Causal inference by using invariant prediction: identification and confidence intervals
Peters, Bühlmann · Meinshausen. Compare assumptions, evidence and scope with the source above. These are editorial companions, not necessarily direct responses.
The Seven Tools of Causal Inference, with Reflections on Machine Learning
Judea Pearl. Compare assumptions, evidence and scope with the source above. These are editorial companions, not necessarily direct responses.
Nonlinear causal discovery with additive noise models
Hoyer et al.. Compare assumptions, evidence and scope with the source above. These are editorial companions, not necessarily direct responses.